Retrieval-Augmented Generation (RAG) is the architecture that gives LLMs access to company-specific knowledge — without fine-tuning. Before every answer the system searches a knowledge base (documents, FAQ, wiki), retrieves the most relevant passages, and hands them to the LLM as context.
A RAG pipeline has four steps: (1) documents are chunked and embedded into a vector database; (2) the caller’s query is also embedded; (3) top-k similar chunks are retrieved; (4) the LLM generates the answer grounded on those chunks.
The decisive lever is rarely the model, it’s data quality: chunk size, deduplication, freshness of sources, and a clear hierarchy between binding and informational documents. A good RAG setup is 80 % data hygiene and 20 % model choice.